I. Overview of the Bioconductor Project. Bioinformatics and Biostatistics Lab., Seoul National Univ. Seoul, Korea Eun-Kyung Lee

Transcription

1 Introduction to Bioconductor I. Overview of the Bioconductor Project Bioinformatics and Biostatistics Lab., Seoul National Univ. Seoul, Korea Eun-Kyung Lee

2 Outline What is R? Overview of the Biocondcutor Project Microarray Installing R and Bioconductor R package structure Vignette Sweave Bioconductor software design

3 What is R? A language and environment for statistical computing and graphics GNU project Combined Math and Stat library Fine graphics Easy and efficient handling of data Rich modern statistical routines

4 Strengths of R Available on a wide variety of UNIX platforms and similar systems, windows and MacOS Easy to produce well-designed publication quality plots, including math. Symbols and formulae For computationally-intensive tasks, C, C++, and Fortran code can be linked and called at run time. Able to write C code to manipulate R objects directly

5 ==> For Novice user, GUI is needed! Differences between R and the other statistical SW R environment characterize as a fully planned and coherent system command line interface (CLI) Preferred for power users Intimidating for beginners Longer learning curve Other SW an incremental accretion of very specific and inflexible tools Graphical user interface (GUI)

6 Bioconductor Project An open-source and open-development software project for the analysis of omics data R add-on packages Provide access to powerful statistical and graphical methods for the analysis Facilitate the integration of biological metadata from WWW Promote high-quality documentation and reproducible research

7 Bioconductor Packages Release 1.9 (Oct. 4, 2006) : 188 packages Designed for R Statistical method : cluster analysis, estimation and multiple testing for linear and non-linear models, resampling, visualization, etc Biological assays : cell-based assay, DNA microarray, proteomics, SAGE, SNP, etc Biological metadata from WWW : GenBank, GO, KEGG, PubMed, etc Interfaces with other languages : C, Java, per, Python, XML, etc..

8 Bioconductor Task View : SW Microarray one channel, two channel, data input, quality control, preprocessing, transcription, DNA copy number, SNPs and genetic variability affy, marray, multtest, limma, etc. Annotation GO, Pathways, Proprietary platforms, Report writing annotate, AnnBuilder,etc. Visualization chromoviz, arrayqcplot, etc

9 Bioconductor Task View : SW Statistics differential expression, clustering, classification, multiple comparison, time course, sequence matching limma, qvalue, affylmgui, timecourse, etc GraphAndNetworks GeneTS, Rgraphviz, etc. Technology microarray, proteomics, Mass spectrometry, SAGE, Cell based assays, genetics Intrastructure Biobase, Rdbi, tkwidgets, widgettools, etc.

10 Microarray DNA microarray chip cdna chip : usually custom based chip Two channel One channel Oligonucleotide chip Affymetrix One channel eg) Agilent, Illumina

11 Central Dogma

12 cdna Microarrays cdna(complementary DNA) A DNA molecule made in vitro using mrna as a template and the enzyme reverse transcriptase. A cdna molecule therefore corresponds to a gene, but lacks the introns present in the DNA of the genome.

13 cdna Microarrays

14 Affymetrix Microarrays

15 Affymetrix Microarrays

16 Microarray Preprocessing Background correction Normalization Summarization Affimetrix : affy Two-channel cdna : marray

17 Microarray exprset : class for microarray data and methods for processing them exprs : the observed expression levels. annotation : character string identifying the annotation that may be used for the exprset instance description notes phenodata : containing the patient (or case) level data

18 Microarray Analysis Differential expression Graph and Networks Clustering Classification Multiple comparison Time course Sequence matching

19 Installing R and Bioconductor R Download from CRAN Bioconductor 1.9 Download from Bioconductor website or From R > source( > getbioc()

20 Starting and quitting R Start : R command Quit : q() Save : save current env. With save.image Working directory : getwd, setwd List objects : ls, objects Remove objects : rm, remove Search path : search, attach, detach Help : help(),?

21 R package structure A structured collection of code, documentation and/or data files : DESCRIPTION, INDEX subdirectories R man doc src, data, demo, exec, inst * package.skeleton

22 How to make R package Linux/Unix R CMD check R CMD build Windows (need to install a couple of SW) Rcmd check Rcmd build Rcmd build --binary *

23 How to install and load R package INSTALL Linux/Unix R CMD INSTALL ---.tar.gz Windows click LOAD library(package.name) packagedescription().find.package() system.file()

24 Vignettes new documentation paradigm an executable document consisting of a collection of code chunks and documentation text chunks provide dynamic, integrated, and reproducible statistical documents that can be automatically updated if either data or analyses are changed Vignettes can be generated using the sweave function from the R utils package

25 Vignettes Each Bioconductor package should contain at least one vignette, providing task-oriented descriptions of the package s functionality. located in the doc subdirectory of an installed package accessible from the help browser, via the help.start function available separately from the Bioconductor website

26 Vignettes Biobase package openvignette function menu of available vignettes and interface for viewing vignettes (PDF) tkwidgets package vexplorer function interactive use of vignettes, stepping through code chunks repostools package

27 Sweave allow the generation of dynamic, integrated and reproducible statistical documents, intermixing text, code, and code output(text and graphics) source file : an executable document consisting of a collection of code chunks and documentation text chunks. utils package : functions Sweave, Stangle

28 Sweave Input (noweb file) Documentation text chunks start text in a markup language like Latex code chunks start with <<name>>= R code file extension.rnw,.rnw,.snw,.snw

29 Sweave Sweave function extract the code chunks, run them and includes their output(text and graphs) in a.tex file and.ps or.pdf files Stangle function concatenates all the code chunks into a.r file Output (.tex or.pdf) a single document containing the documentation text, the R code, the code output (text and graphs) automatically regenerated whenever the data, code or documentation text change

30 Sweave Stangle main.rnw main.r fig.eps Sweave main.tex fig.pdf latex pdflatex main.dvi main.pdf dvips main.ps

31 Biocouductor SW design programming approaches used in Biocouductor Object-oriented S4 class/method framework to deal with data complexity, to represent and manipulate various data types Environments to provide mappings between different gene identifies in the annotation metadata packages closures for software modularity and extensibility

32 Experimental Metadata gene expression measures scanned image (TIFF) image quantitation data (.gpr or.cel ) normalized gene * array matrices of expression measures ( log ratios or summary measures) reliability/quality information probe sequence information information on the target samples hybridized to the arrays : clinical covariates, experimental condition, etc.

33 Experimental Metadata Standard form MIAME : minimum information about a microarray experiment MAGE-ML : microarray gene expression

34 Annotation Metadata Biological attributes that can be applied to the experimental data for gene, chromosome location gene annotation (LocusLink, GO) relative literature (PubMed) Biological metadata sets are large, of different types, evolving rapidly, and typically distributed via the WWW annotate, annaffy, AnnBuilder

35 Data complexity large p, small n dynamic/evolving data multiple data source : WWW, in-house multiple data type quantitative qualitative text, graphical image, sound censored, missing, erroneous data various levels of processing

36 Object-Oriented Programming adapt OOP paradigm in order to deal with the complexity of experimental and annotation metadata S4 class/method design allows efficient and reliable representation and manipulation of large and complex biological datasets of multiple types advantages of class/method design keep all relevant information in one object print, summary, accessor/assignment, subsetting, and more specialized methods Tools for programming using S4 : methods package

37 OOP : Classes provide a software abstraction of a real world object. It reflects how we think of certain objects and what information these objects should contain defined in terms of slots which contain the relevant data An object is an instance of a class A class defines the structure, inheritance, and initialization of objects

38 OOP : Methods and Documentation Methods function that performs an action on data define how a particular function should behave depending on the class of its arguments allow computations to be adapted to particular data types Documentation special commands can be used to provide and access documentation for S4 classes and methods, using the type? topic syntax. Methods available for a particular class are listed in the class help file

39 OOP : exprset class defined in the Biobase package used to represent processed expression measures from either Affymetrix or two-color spotted microarrays slots exprs : matrix of expression measures se.exprs : matrix of SEs for expression measures phenodata : sample level covariates and responses description : MIAME information annotation : name of annotation data notes : any notes

40 OOP : phenodata class defined in the Biobase package used to keep track of information on target samples hybridized to the microarray varlabels : list of variable labels pdata : dataframe of sample level variables arrays * variables

41 affy : Affymetrix Oligonucleotide chips affy package class definitions for probe-level data and basic methods for manipulating microarray objects (printing, plotting, subsetting, class conversions, etc.) AffyBatch : probe-level intensity data for a batch of arrays ProbeSet : PM, MM intensities for individual probe-sets

42 marray: two-channel spotted microarrays marray package class definitions for two-color spotted DNA microarray data and basic methods for manipulating microarray objects marraylayout : information on microarry layout marrayraw : pre-normalization intensity data for a batch of arrays (same layout) marraynorm : post-normalization intensity data for a batch of arrays.

43 pubmedabst class annotate package provide a pubmedabst class for storing PubMed abstracts pmid authors absttext articletitle journal pubdate

44 Annotation : matching IDs Accessing annotation information from databases such as GeneBank, GO, or PubMed, presupposes the ability to perform the following essential bookkeeping task mapping between the different identifiers (IDs) for a given gene. one GENENAME one GenBank accession number several different GO term IDs several different PubMed IDs

45 R Environments provide key-value mappings similar to hash tables in other languages The term key refers to the name of variable, which can have different values in different environments. functions for working with environments include ls get, mget(base), multiget(biobase) assign(base), multiassign(biobase)

46 R Environments keys can be accessed using ls(name of the environment) Values can be accessed using get(key, envir=name of the environment) mget(keys, envir=name of the environment)

47 Closures consists of the body of the function along with an enclosing environment containing all variable bindings needed for evaluating the function. Closures facilitate software modularity and extensibility

48 Summary Overview of the Biocondcutor Project Microarray R package structure Vignette Sweave Bioconductor software design Next session : focusing on Microarray data analysis